Explore decentralized deep neural network architectures in this 40-minute lecture delivered by Anestis Kaimakamidis as part of the AI Doctoral Academy's AICET2025 program focused on cloud and edge computing applications for deep learning and big data analytics. Examine how distributed DNN systems can be designed and implemented across cloud and edge computing environments to optimize performance, reduce latency, and enhance scalability for large-scale machine learning applications. Learn about the architectural principles, design patterns, and implementation strategies for deploying neural networks in decentralized computing infrastructures, with particular emphasis on the intersection of deep learning methodologies and distributed computing paradigms for big data processing.